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Covid-19: Why Some States Have Lower Death Rates

By Brian Woolf
June 23, 2020

Quick question. What's your state's current population? And its size (in square miles)? If you divide the population by the area, you'll learn how many people per square mile (PPSM) live there. That number is your state's density. It also suggests your Covid-19 destiny...

Why? Because where your state lies on the density ranking is approximately where your state lies on the virus's death rate ranking. [The Death Rate (DR) is the percentage of a state's population that dies from the virus.] A study of the 50 states will show you that, as a general rule, the greater its population density, the greater its likelihood of having a higher death rate. There are exceptions in every trend, both better and worse. This trend is no exception. Some variances from trend are caused by factors within a Governor's control: others outside.

Besides density, other major contributors to a state's death rate include large events (which are a special form of density), the obesity level of its citizens, the cleanliness of its cities, the quality and priority given to nursing-home care and, of course, the decisions and effectiveness of a state's Governor.

Knowing that density contributes significantly to a state's death rates helps us understand where we are today and provides a decision-making framework to manage our Covid-19 (CV) challenges and navigate our still uncertain future.

Note: almost all data used is from the free CV website Worldometers.info on Jun 12, 2020.

The Twins: Density and Death Rate
To illustrate the relationship between a state's density and death rate, we will compare our lowest and highest density states, Alaska and New Jersey. Alaska has an average of 1 person per square (PPSM) while New Jersey has 1,025 PPSM. A huge difference. Likewise, there is a dramatic difference in their death rates. New Jersey's 0.1410% of its population is over eighty (80) times that of Alaska's 0.0016%, as seen below.


It's important to know that a similar relationship occurs within a state that has a large, high-density city. A classic example is New York where the death rate of New York City is almost five (5) times that of the rest of the same state, as the following table shows. The density difference between the two is noticeable: NYC has over 27,000 PPSM while the rest of the state has only 204 PPSM. To repeat the point, in the YTD June 12, the average high-density NYC citizen's chance of dying (0.371%) was almost five times greater than if he or she lived elsewhere in NY state (0.081%), where the density is dramatically lower.


Why is this? A simple explanation is that when living in a city that has over 27,000 PPSM, you have a much higher chance of being infected by CV as you walk its crowded streets, travel on its crowded subway, shop in tightly-packed stores, work in close conditions, attend large stadium events, and return each night to your small condo in a multi-storey building. In other words, the denser your state or city, the greater is the number of people you will be in contact with every month.

This explanation is reinforced when we look at the bottom end of the density scale. You will not be surprised to learn that low-density states rarely have big, large cities. In fact, there are four low-density states that have no cities over 100,000 people, as seen in this table.


With the largest city of these four low-density states averaging 55,500 people, you know that urban density is not one of their problems and nor is a high-exposure to a large number of different people as, say, New York City has. Thus, the nature of their CV challenges is different.

The Norm Form Method of Understanding and Managing Covid-19
One effective way to understand a wide range of information is to rank the key data, then break it into peer groups to allow benchmarking and identifying areas for improvement. For example, if you are working with a large number of stores, sort them from highest to lowest monthly sales and break them into a number of groups based on sales. Then you are comparing stores with similar sales in each group rather than against all stores. Why is this important? Because high-sales stores have different cost structures and challenges versus low-sales stores, eg, labor costs are a lower percentage of sales in higher volume stores. A Norm Form simply identifies the "normal" results for each group.

After reviewing the CV information publicly available it was obvious that ranking US states and using Norm Forms for the different density (and death rate) levels would be the best way to identify and manage the different CV challenges. So, the 50 states were placed into one of five Norm Forms. Norm Form 1 comprises the 10 states with the lowest density levels and Norm Form 5 the highest. Accompanying comments will explain some of the variances from the "norm." This will provide ideas on how to explain other differences from the norm that you identify.
First, we'll start with an overview.


The Norm Form Summary, above, is a summary of the five Norm Forms that will follow for discussion. The top half of the table shows the averages of the 10 states in each Norm Form (NF); the bottom half shows their totals. On the very bottom line, we see:
·  the population of 50 states 330,600,000 (expressed in thousands)
·  the area of the 50 states 3.8 million sq miles
·  the 50-state overall density 87 people per square mile (PPSM)

Then, columns (f-m) show year-to-date CV totals (and their percentages):
·  23m people were tested, ie, 7.0% of the US population (see col. f & i);
·  2m CV-infected Cases were identified by the tests, ie, 8.9% of all Tests (see col. g & j);
·  114,000 CV-attributed deaths, ie, 5.6% of those infected, and 0.0345% of the total US population (see col. h, k & l). Translating that last number from Math into English, it says that the number of total CV deaths were about mid-way between three and four hundredths of 1% of the US population.
·  The above points can be restated this way: 7% of the US population has been tested for the virus; of that number 8.9% were found to be infected; and of those infected 5.6% died. For readers with a mathematical mind, if those three elements are multiplied (7.0% x 8.9% x 5.6%) we find the same 0.0345% of the US population who have died.
·  The right-hand column (col m) shows the percentage of Cases still Active (ie, not yet cured or died). It's included because it's is an important indicator: a high number indicates there are more potential deaths than a low number does. [Be aware that the number of Cases Hospitalized and in ICU are not readily available for all states and thus not included.]

Two other key pieces of information to note on the Summary are the Density and Death Rate (cols e & l). Reading downwards from NF1 to NF5, you'll see that as density increases, the death rate rises. The difference between NF1 and NF5 suggests that different solutions may be required for our top and bottom NF tiers, with variations in between.

Norm Form 1: The Lowest-Density States
NF1 comprises our 10 states with the lowest density, ie, the lowest average number of people per square mile among the 50 states (see column e). The death rate (DR) of NF1 is also the lowest as a group among the 50 states. There are anomalies inside each group, of course, and one purpose of the NF is to identify them and understand why.


We'll start with the Death Rate. At the bottom of col. l, we see the average DR% for the 10 states of NF1 is 0.0101% whereas the DR for all 50 states is 0.0345%, over three times bigger. NF1's DR of 0.01% is one hundredth of 1% of the US population. To put that in perspective, think of a town in your state of 10,000 people in which, over a 5-month period, one person died of the virus. That's it, and it tells us that low density-states have not been significantly affected by the virus.

Exploring further, the underlying picture for low-density states is even better as there were several high DR anomalies scattered among the group: in particular, New Mexico (NM) and Nevada (NV). I shall explain NV's higher than-its-peers death rate of 0.0147% (row 9) because the cause deals specifically with density.

The biggest city in NV is Las Vegas. It attracts 50 million visitors each year, including many from virus-heavy New York, providing each year the third largest number of direct flights to Las Vegas. As you know, most visitors to Las Vegas flock to the casinos (whether gambling or not), enjoy well-filled shows, and walk up and down the densely crowded Strip. Would you not suspect this exciting tourist trap is a perfect location for unaware CV superspreaders to infect others as they walk through the crowds? Las Vegas's challenge is similar to NYC's-the challenge of large anonymous crowds-which comprise non-spreaders, regular spreaders, and superspreaders. NV is a low-density state that has an extremely-high transient tourist city in its midst. And it's not the only state with this type of challenge.

On the other hand, one positive highlight of the NF1 group is that seven (7) of its 10 states have a DR less than one one-hundredth of 1% (ie, under 0.01%)! Of those seven, one (WY) has no city greater than 100,000; three others have only one city in the 100,000-200,000 range: MT (Billings 110,000), ND (Fargo 130,000), and SD (Sioux Falls 191,000). All seven states have low state and urban densities that are associated with low death rates. It's an example of the density-death rate twins.

Let me insert here something not included in the official CV statistics but which affects our decision-making going forward. Do Lockdowns work everywhere? Let's see. Seven states chose not to participate in the CV Lockdown program triggered on March 11. Four are in this NF1-Lowest Density group-WY, ND, SD, and NE (rows 2, 4, 5, and 8). The other three, in NF2, are UT, IA, and AR (rows 12, 15, and17.) Here's a serious question: Can you see anything in the results of these seven states that, when compared to their peers, suggest their No-Lockdown decision was a bad one? Second question: Are Lockdowns the right prescription for all states?

Norm Form 2: The Low-Density States
The states that comprise NF2 have densities ranging from 38 to 65 PPSM. This compares to NF1's range of 1 to 35 PPSM. Both Norm Form groups have some similar characteristics. One is that NF1 has seven states with death rates less that one one-hundredth of 1%, ie, less than 0.01%. NF2 remarkably continues that trend with six such states (see col. l, rows 11, 12, 13, 16, 17, and 20). One benefit of Norm Form analysis is that it raises the question: what about the others? Why didn't they? What circumstances were different in their states?

As my intent with this post is not to bury you in a highly-detailed commentary of the many questions the NF numbers suggest (such would require a much-lengthier document) but to point out various NF features to encourage you to do your own exploration. If so inclined, use these forms, updating them from time-to-time from worldometers.info The trick is to understand how the different pieces of data tie together and then identify and understand those elements that reflect excellence-and those that don't.


For example, I kept looking for states with 'better-than-trend" death rates and then research their "why and how?" One such search in NF2 was about Arkansas's (AR) low DR of 0.0058% (row 17) compared to its neighboring state, Mississippi (MS) which has a DR of 0.0295%, five time bigger (row 18). I learned that AR's 0.0058% death rate (six one-thousandths of 1%), came primarily from effective leadership and management. When the virus first hit the nursing homes of Washington state (WA), AR's governor quickly sent people to study the situation both there and elsewhere, both in the US and other countries. A quick decisive decision was made to ring-fence AR's nursing homes. It worked, aided by other supportive actions. Other governors did likewise with similar results-but, unfortunately, not all did.

The other curious element about AR is that its low death rate did not come about from the full Lockdown that 43 other states imposed. AR was one of the seven states that chose against imposing a societal Lockdown.

What else is different about AR's numbers? We see (row 17), that AR is in line with NF2's average CV testing rate (AR tested 5.9% of the state's population vs the group average of 5.8%). And AR was a tad better than the group with the percentage tests what were found to be infected [what CV call "cases"] ie, 6.4% vs 7.1%. But what is remarkable, is their very low percentage of cases-only 1.5%-that ended in death. That means that, in AR, 98.5% of all who were infected with the virus survived. Only three other states, out of all 50, have a lower "deaths from cases" rate (SD row 5, NE row 8, and UT row 12). These four states need to be studied carefully -their tracking, case care, and hospital practices-to learn what they are doing to have so few of their CV-infected citizens die.

Norm Form 3: The Medium-Density States
West Virginia (WV), row 22, was the big surprise to me in NF3. WV, in this middle-of-the-pack group of 50 states, has a death rate of 0.0048% (one two-hundredth of 1%) a standard only four of the NF1 lowest-density states achieved. What makes WV special? Well, it's that no-huge-urban-area trump card. WV's four largest cities, Charleston (47,000), Huntington (46,000), Morgantown (31,000), and Parkersburg (30,000) total 154,000-less than 10% of the state's population! The major city of many states is over 10%. What it means is that WV's population is spread thinly throughout the state without any large urban areas. It's the opposite of what we will later see in NF5 in the high-density North-East states.

Louisiana (LA), row 24, was another surprise, but in the opposite direction. It's relatively high DR of 0.0646% (six tenths of 1%) seems to be the result of unfortunate ignorance. The virus surreptitiously "launched" itself during LA's annual monster New Orleans Mardi Gras festivities in late February-before the nation was fully aware of its potential severity. Subsequent wide testing (10.3% of LA's population) found that almost one in ten of them (9.4%) were infected and, of those, almost 7% later succumbed. This is an example of what I call the Big Event Density Challenge to which a healthy solution is still being sought.

And then there's Texas (TX), row 29. Strong leadership and a good health system are seen in its low 2.3% percentage of cases ending in death and their close-to-WV low DR (0.0066%) despite three large cities: Houston (2.3m), Dallas-Fort Worth (2.2m) and San Antonio (1.5m). Remarkable results, and worthy of study and benchmarking by other big-city states.

Norm Form 4: The High-Density States
In NF4, the biggest positive variance from the norm is Hawaii (HI), row 31. Its YTD death rate is the lowest of all 50 states-close to one one-thousandth of 1% (0.0012%). And that's not likely to change much in the near future as only a very low 7.5% of all cases are still active.
What's HI's "secret?" This message on a HI state website explains it: All individuals, both residents and visitors, arriving from out-of-state to Hawaii through July 31, 2020 are subject to a mandatory 14-day self-quarantine. The mandate applies to all arrivals at state airports, including private and commercial aircrafts.


In addition, HI actively discourages visitors from coming at all. Airline traffic has fallen from the skies. The actions taken by HI is akin to those of New Zealand (see previous post), an island nation which imposed a total blockade against all visitor from overseas. It helped NZ completely eradicate, recently, the virus by blocking new CV sources from overseas. HI's response is similar, but not quite as extreme. But we know its outcome: very few cases and an ultra-low death rate. As an aside, Alaska (AK), row 1, which is also able to geographically isolate itself, demands that everyone entering the state must undergo a 14-day quarantine. And it is serious. A $45,000 fine is imposed for breaking the quarantine. Its DR is 0.0016%, just a tad higher than HI's.

HI's results incorporate other attributes that minimize CV contagion. Its obesity scores are very low. This is an important indicator because obese people often harbor immune-weakening ailments including hypertension, heart disease, diabetes, and cancer. HI is also rated as one of the healthiest states, aided by a sunny climate and outdoor living. CV experts tell us being "inside" is a fertile contagion area.

Unfortunately, there is a somber unhealthy side note: about 25% of all HI's jobs are tourism-related, so it's now suffering high unemployment. And, without a CV vaccine, one wonders when tourists will be allowed to visit again thereby putting earning power back into now-unemployed hands. Until then, the state will nurse some serious economic consequences of their CV decisions. A discussion on the economic consequences of Covid-19 will follow in a later post.

In NF4, the biggest negative variance from the norm is Illinois (see row 39). IL has the 12th highest density among the 50 states, and hosts the third-largest city, Chicago. It's density profile (and death rate) is the antithesis of states like WV.

Chicago, occupying Cook County, the second most populous county in the US, houses 41% of IL's population, yet accounts for 64% of IL's CV cases, and 66% of CV deaths. Its CV death rate approaches three times that of those who live in IL outside of Cook County: 0.0836% vs 0.0295%. It reminds us again that states with large urban communities have an added challenge combatting CV due to unique factors associated with their density.

Incidentally, in case you'd like to have an interesting experience and do your own research on two states with similar densities, one worse than trend, the other better, I suggest you consider Michigan (row 27) and Tennessee (row 34). It's a research-rich, fascinating contrast that touch many of the points covered in this paper from density to leadership to obesity to nursing homes.

Norm Form 5: The Highest-Density States
To make the discussion of NF5 flow more easily, the ranking of the states has been changed from density (col e) to the death rate (col l). This rearrangement creates three natural groups without significantly changing the density ranking.


The premise of this post is that, in the United States, population density is a strong driver of the death rate of people infected by CV. Therefore, one would expect that the 10 Highest Density States have the highest death rates. And it largely works out that way as seen in the above table.
The states (FL, OH), in the first two rows, are at the bottom end of NF5's density and death rate rankings. They are not "members" of the "Northeast cluster" comprising the eight other states. The cluster has two parts and is seen visually below. The cluster is composed of a contiguous outer core (DL, MD, and PA) which is geographically to the south and west of the inner core (MA, RI, CT, NJ, and NY which is the hub).

Florida and Ohio. Their death rates (listed in col l) are better than trend, attributed to their Governors who forcefully and, at the time, somewhat unpopularly, implemented courses of action that helped their states reduce the potential spread of the virus. Ohio introduced tight controls, including the unprecedented action of cancelling its scheduled state-wide, in-person voting in its important political primary. Florida, with the nation's highest percentage of elderly (age 65+) started earlier than most states in focusing effort - in a major way- specifically on them. Nursing homes were quarantined, infected seniors were separated from the non-infected, and nursing staff were tested daily. FL was one of the leaders in this. And later, when New York had its virus outbreak, FL imposed limits on NY visitors. For a vulnerable state, such early decisive leadership prevented many deaths. Despite the death rates of these two states being better than the trend (FL is between one and two one-hundredths of 1% and OH is just over two one-hundredths) both are acutely aware of their approximately 75% Active/Total Cases ratios which indicate that a lot of new cases have recently appeared as citizen movement limitation regulations have been loosened. Further study of both states will yield valuable best practices.

The Inner and Outer Cores. As it relates to CV, New York (and its vibrant, dominant heart, NYC) is the epicenter of an inner and outer core of states. Right across the river from NYC (which is the epitome of US urban density-over 8m people packed into 300 sq m, averaging over 27,000 PPSM) is NJ, America's most densely populated state (over 1,000 PPSM) which is intimately intertwined with NYC. To NYC's north are three more highly connected states, MA, RI, and CT (three states with a combined density of 663 PPSM). This inner core of five high-density, highly-interrelated states is the poster child for being the center of America's CV epidemic, "supported" by three outer-core, less-directly-connected states-DL, MD, and PA.

The numbers in the bottom five rows of NF5 (col e, k, and l) clearly portray the situation:
·  The Lower 40 states (NFs 1-4) have a density of 205 PPSM. FL-OH has 305. The Outer Core (DL, MD, and PA) has 326. And the Inner Core (RI, MA, CT, NJ, and NY) has 494.
·  Deaths as a percentage of (infected) cases is 3.9% (Lower 40), 4.8% (FL-OH), 6.2% (Outer), and 7.6% (Inner).
·  Deaths as a percentage of their respective populations (ie, the death rate) is 0.019% (in the Lower 40 states), 0.016% (in FL-OH, better than Lower 40, reflecting the effective management of these two states), 0.048% (in Outer, a rate 3x the size of FL-OH), and 0.140% (in Inner, which is almost 9x the size of FL-OH).

The above highlights again the density-death rate trendline. We have seen how, in NF5, FL-OH performed better than the trendline while, in my opinion, at least four of the five Inner Core states performed worse than the trendline. Part of the problem may lie in the quality and effectiveness of its case care and patient/ hospital management. How else can one explain the Inner Core having double the death rate of infected cases compared to the lower 40 states: for every 100 infectious cases in the 5 Inner Core states 7.6 people died vs 3.9 in the Lower 40?

Closing Comments
This post started as an exercise to educate myself about Covid-19. I was frustrated by the news and the monologues. Like our current political landscape there are lots of emotive and abstract words flying around about the virus but very few helpful, concrete words. Yes, numbers were tossed around but usually to frighten and scare and sell newspapers. But there has been no integrated framework of numbers used as one sees in some of our better-managed businesses. Thus, this paper.

Some of the lessons I learned from my research include:

1. As a general rule, the higher the density, the higher the CV death rate % (with the usual variances.) Higher density means more people per square mile, which means that, on average, you come in contact with more different people each week, which means in your everyday life you come in contact with more potential infected Covid-19 people, which means a higher chance of being infected and possibly dying.

2. The highest density states are in the Northeast clustered around New York City. They are that way because they are highly urban in small-sized states. The average population of our nation's 50 states is, as seen below, 6.6m per state, with an average area of 76,000 square miles, creating a density of 87 people per sq. mile. In contrast, our 5 densest states which are centered on NY (ie, RI, MA, CT, NJ, NY) have an average population of 8.0m, which is 21% higher than the average of all states, but in an average area of 16,000 square miles, 21% the size of the average state or, said another way, 79% smaller). This results in an average density of 494 people per sq. mile, 567% greater than our average state. That means a person living in this 5-state area will likely meet many more people each month than in an "average" state.

Obviously, that happens because we see that even though the average population of these 5 states was 21% greater than the 50-state average, their number of CV deaths was almost five times (490%) greater. And remember, overlooked in these population comparisons, large cities in densely populated states, such as NY, have major airports, big sports stadiums and entertainment attractions which add to the flow-through of people one may be exposed to there.

3. High density cities are not just in the Northeast. They are scattered throughout the country, like Chicago and Los Angeles, and high-density entertainment cities like Orlando, New Orleans and Las Vegas. They all share a similar challenge.

4. Seven states opted out of having a "Total Lockdown"-WY, ND, SD, NE (see NF1) and UT, IA, AR (see NF2.) When comparing their data to that of their peers who did lockdown (see NF1-NF2) it is difficult to see that their CV health performance suffered because of it. Not just that, their citizens, businesses, and even their state budget benefited economically from being "open."

5. Leadership by governors and their teams have significantly contributed to the variances from the trendline-in both positive and negative directions. I learned of governors that I have come to admire, such as the seven who had the vision, courage, and conviction in their belief that their states would be harmed less overall by not following in Lockdown lockstep ... and then had to then effectively manage a successful outcome for their states, which they did. I admire the self-starter, result-oriented governors who didn't wait for Washington DC to give directions and help but initiated their own research and, for example, discovered the key vulnerability of nursing homes and set up a ring-fencing program around them-they did it because they had done their research and believed it was right-and they were. I admire the governor who cancelled his state's in-person election because it would be hypocrisy to preach safe distancing but then make an exception for the polling booth. It was a time of great uncertainty-fearful hyperbole flowed freely-and governors had to ingest input, make decisions-and make them work-knowing that all of them wouldn't be right.

6. Several thoughts flow from the five points above. The first is that we should avoid one-rule regarding CV for all states wherever possible, for they are all different with different density rates and characteristics; and have different risk characteristics. A sledgehammer is not the only way to crack open a nut. Second, I suspect risk profiles will change when the Government stops shoveling out cash to Lockdown states and all states become economically responsible for their CV rules and regulations.

7. Until a miracle fix-all CV vaccine is available and accessible, it seems that the biggest challenge we have is to solve the higher death risk that currently goes with higher urban density and its oft-associated CV-spreading characteristics (eg, dirty, crowded subways and transport systems, unclean streets, homelessness.) I hope we have sent a small, smart task-force to big cities like Tokyo (population 37m) one of the cleanest cities and tightly-cramped but cleanest subways (8m passengers daily) in the world, and to Seoul (population 10m) another very clean city that has made excellent use of technology, including cell phones tracking, to fight CV. Both cities and their countries have suffered inconsequential CV deaths this year. YTD June 23, Japan (pop. 126m) has had 955 deaths [compare Rhode Island (RI) with 906 deaths]; South Korea (pop. 52m) has had 281 deaths [compare Kansas (KS) with 262 deaths.] Think our big cities may be overlooking a CV learning opportunity as they seek better ways to manage their challenge?

And one closing thought: Managing a business focuses on four key numbers: sales, gross margin, expenses and profits, with profits being the critical end result. Managing Covid-19 has four key numbers, too: Tests, Cases, Deaths, and the Death/Population Ratio.

Our CV management report for the YTD thru June 12 reads:
·  Across 50 states: 7.0% of the population were tested.
·  Of those tested: 8.9% were found to be CV-infected (and described as "cases")
·  Of those infected: 5.6% have died
·  Creating a DR of: 0.03% (equivalent to three people in a town of 10,000 people)

And-if we consider the 99.4% population "out and about" (not in a nursing home) the above means their DR was about 0.02% (equivalent to two people in a town of 10,000 people). That's what our CV fight has been about over the past 5 months, just 0.02%. Have the economic and social costs (lockdowns, bankruptcies, etc) and human costs (depression, drugs, alcohol, delayed medical treatment, etc) been a good investment? And, looking forward, what will we change?

Copyright © 2020 Brian Woolf

About the author...

Besides a full business life in retailing, and later, loyalty marketing, the other part of Brian Woolf's life has been filled with diverse interests: particularly speaking (including Toastmasters), travel (including all seven continents), and reading (including history). And he has written seven books sharing what he has learned along the journey. Ask him, two favorite trips? Antarctica and the Nile. Ask him, two favorite books? The Lessons of History (Will & Ariel Durant) and Over the Edge of the World (Laurence Bergreen). He loves learning and sharing.

 
Copyright © 2020 Brian Woolf